Teaching

ME 3227 Design of Machine Elements (Spring 2021, Spring 2022)

In this course, the material is presented in the context of the overall design process, but emphasizes the quantitative methods used to size machine elements. Machine design covers a wide range of topics, and a subset of the needed materials is selected based on the combination of:

  1. General ideas about how each part of knowledge integrates into the machine design.
  2. Analytical, semi-analytical, and empirical methods that are most likely to be encountered.
  3. A variety of topics in the machine element design.

ME 3295 Computational Foundations of Digital Manufacturing (Fall 2021)

The purpose of this course is to introduce students to the multiple components that integrate to create future manufacturing. This introductory class will explore modern data science and AI (e.g., Bayesian Learning and Inference, Deep Neural Network, and Reinforcement Learning) and will aim to quickly learn data visualization, integrate various sensors with machines, perform process monitoring and control, take test measurements using machine visions, explore the process of discovering causality structure, and finally introduce cloud computing and digital security and privacy to control multiple machines.